The parameterized complexity of motion planning for snake-like robots

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Abstract

We study a motion-planning problem inspired by the game Snake that models scenarios like the transportation of linked wagons towed by a locomotor to the movement of a group of agents that travel in an “ant-like” fashion. Given a “snakelike” robot with initial and final positions in an environment modeled by a graph, our goal is to decide whether the robot can reach the final position from the initial position without intersecting itself. Already on grid graphs, this problem is PSPACE-complete [Biasi and Ophelders, 2018]. Nevertheless, we prove that even on general graphs, it is solvable in time kO(k)|I|O(1) where k is the size of the robot, and |I| is the input size. Towards this, we give a novel application of color-coding to sparsify the configuration graph of the problem. We also show that the problem is unlikely to have a polynomial kernel even on grid graphs, but it admits a treewidth-reduction procedure. To the best of our knowledge, the study of the parameterized complexity of motion problems has been largely neglected, thus our work is pioneering in this regard.

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APA

Gupta, S., Sa’ar, G., & Zehavi, M. (2019). The parameterized complexity of motion planning for snake-like robots. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2019-August, pp. 5670–5676). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/786

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